At present, face recognition systems are more and more applied to scenarios that require an ID authentication in fields like security, finance etc., such as remote bank account opening, access control system, remote transaction operating verification etc. In these application fields with high security level, in addition to ensuring that a face similarity of an authenticatee matches with library data stored in a database, first, it needs that the authenticatee is a legitimate biological living body. That is to say, the face recognition system needs to be able to prevent an attacker from attacking using pictures, 3D face models, or masks and so on.
The method for solving the above problem is usually called living body detection, which aims to determine whether an obtained physiological feature comes from a living, in-field, real person. Living body verification schemes acknowledged as mature do not exist among technology products on market, and the conventional living body detection techniques either depend on specific hardware devices (such as infrared came, depth camera) or can prevent only simple attacks from static pictures. In addition, most of the living body detection systems existing in the prior art are cooperated-style, i.e., requiring a person being tested to make a corresponding action or stay fixed in place for a period of time according to an instruction from the systems, so it will affect user experience and efficiency of living body detection. Besides, for example, the accuracy and robustness of other methods by determining whether there is an image border in a detected image can hardly meet the actual demands.